Genetic differentiation of a southern Africa tepary bean (Phaseolus acutifolius A Gray) germplasm collection using high-density DArTseq SNP markers

Genetic resources of tepary bean (Phaseolus acutifolius A. Gray) germplasm collections are not well characterized due to a lack of dedicated genomic resources. There is a need to assemble genomic resources specific to tepary bean for germplasm characterization, heterotic grouping, and breeding. Therefore, the objectives of this study were to deduce the genetic groups in tepary bean germplasm collection using high-density Diversity Array Technology (DArT) based single nucleotide polymorphism (SNP) markers and select contrasting genotypes for breeding. Seventy-eight tepary bean accessions were genotyped using 10527 SNPs markers, and genetic parameters were estimated. Population structure was delineated using principal component and admixture analyses. A mean polymorphic information content (PIC) of 0.27 was recorded, indicating a relatively low genetic resolution of the developed SNPs markers. Low genetic variation (with a genetic distance [GD] = 0.32) existed in the assessed tepary bean germplasm collection. Population structure analysis identified five sub-populations through sparse non-negative matrix factorization (snmf) with high admixtures. Analysis of molecular variance indicated high genetic differentiation within populations (61.88%) and low between populations (38.12%), indicating high gene exchange. The five sub-populations exhibited variable fixation index (FST). The following genetically distant accessions were selected: Cluster 1:Tars-Tep 112, Tars-Tep 10, Tars-Tep 23, Tars-Tep-86, Tars-Tep-83, and Tars-Tep 85; Cluster 3: G40022, Tars-Tep-93, and Tars-Tep-100; Cluster 5: Zimbabwe landrace, G40017, G40143, and G40150. The distantly related and contrasting accessions are useful to initiate crosses to enhance genetic variation and for the selection of economic traits in tepary bean.


Introduction
Do the authors identify other literature on the topic and explain how the study relates to this previously published research?R// In general, the authors have reviewed the current literature, but I suggest adding the most recent works in the topic.For instance, Bornowski et al. in

Are the figures and tables clear and readable? (Keep in mind that depending on the submission system you're working in, you might have to click a link to view the high-resolution versions of the authors' figures).
R// Yes, but: 1.In Figure 3, please list each subplot and change the Eigenvalues of each PC plot to the variance explained of each.PC. 2. In figure 4 organize from largest to smallest according to each percentage of subpopulation and add the accession code at the bottom.In figure 6 add the percentage of variance explained by each component Is the presentation appropriate for the type of data being presented?R// Yes, but please add the reference to the R-package used for plotting.For example, did authors use the CMplot function for Figure 2?
Do the figures and tables support the findings?R// Yes but it's imperative adjusts the figures and tables.

Methods
What experiments or interventions were used?R// The authors genotyped 78 accessions of tepary bean by means of the GBS methodology using the first version of P. acutifolius, to obtain 10.527 markers after the SNP Quality Control.Then, the authors carried out a genetic diversity and population structure analysis.

Are the experiments or interventions appropriate for addressing the research question?
R// The experiments and the analytic methodology are appropriate for addressing the research question.However, I suggest providing greater clarity regarding the function used for the ancestry analysis by LEA.Was the algorithm employed for this approach SMNF (e.g., SMNF is an optimization of Structure)?The variant calling process also needs a more detailed explanation.It's unclear how this process was conducted.While they used DArTsoft, a software dedicated to DArTseq, it's worth noting that the use of this tool for GBS is not common.Typically, the main bioinformatic approach for GBS involves tools such as GATK, TASSEL, Stacks, etc.
Are conditions adequate and the right controls in place?R// Yes, they used a diversity panel of tepary beans that include several races and origins.
Is there enough data to draw a conclusion?R// Yes, but I advert that the using of this panel for future association analysis would need major number of genotypes accessions.

Results, discussion, conclusions
Do the results support the conclusions?
Yes, but I recommend specifying some analysis.In that sense, from data science, the visualization of the first two principal components does not suggest an optimal number of components (PCs) but rather it is a clustering validity analysis that suggests the optimal number of clusters.
2023 published a study on genotyping many accessions of tepary beans.On the other hand, I recommend including more references to analytic tools that can be used in this work related to population structure, etc.Additionally, to provide context on how this study relates to previously published research, I suggest including other works on population stratification using GBS in common beans such as Cortés & Blair 2018 or Dartseq as Valdisser et al 2017; or studies involving interspecific panels (Common Bean × Tepary Bean) like those by Cruz et al. in 2023.